Speech-to-text for AI medical scribes: Why clinical vocabulary breaks generic STT

TL;DR: Generic STT engines fail in clinical environments because language model probability overrides correct acoustic detection of medical terms, substituting phonetically plausible but clinically wrong candidates silently. The result corrupts drug names, dosages, and diagnoses before the LLM ever sees them. Before selecting an STT engine for a medical scribe, verify four things: whether vocabulary biasing works at inference time without fine-tuning, whether async diarization accurately separates clinician and patient audio, whether the model holds up on noisy consultation recordings rather than clean read-speech, and whether the vendor's data training policy covers PHI by default on your plan.

Migrating from self-hosted Whisper to a managed speech-to-text API

TL;DR: Self-hosting Whisper's true cost rarely sits in the model weights. GPU idle time, VRAM leaks under parallel load, and the engineering hours spent maintaining CUDA dependencies and diarization pipelines are where the bill compounds. For teams processing under roughly 3,000 hours per month, assuming 20% of one US FTE at $150K loaded annual cost, a managed API is cheaper, though the break-even shifts materially against your actual labor cost. Above that threshold, the decision depends on your DevOps overhead and whether audio accuracy on real-world recordings matters for downstream systems like CRM sync and coaching scores.

Migrating from AssemblyAI to Gladia: A step-by-step switching guide

TL;DR: Switching from AssemblyAI requires four concrete changes: update one auth header, remap batch endpoints, adjust the JSON response schema, and resample audio for WebSocket connections. Multiple customers independently report completing these in under a day with a rollback abstraction layer in place. The bigger structural difference is cost model: a production stack with diarization, sentiment, entities, and summarization runs $0.30/hr on AssemblyAI's Universal-2 tier because each feature is metered separately, versus a bundled base rate. This guide covers the exact parameter mappings, payload diffs, WebSocket reconfiguration, and a zero-downtime cutover strategy.

Migrating from Deepgram to Gladia: A step-by-step switching guide

TL;DR: Migrating from Deepgram to Gladia is primarily an endpoint and parameter mapping exercise, not a rewrite. The authentication header changes from Authorization: Token to x-gladia-key, most query parameters map directly, and we bundle diarization, translation, and sentiment analysis into the base rate on our Starter and Growth plans rather than charging them as add-ons. Using shadow testing and feature flags, engineering teams can complete the cutover with an instant, flag-controlled rollback path and no production downtime.

HIPAA-compliant speech-to-text: BAA, PHI redaction, and vendor selection

TL;DR: HIPAA-compliant speech-to-text requires three layers working together: a signed Business Associate Agreement before any clinical audio reaches your vendor, automated PHI redaction covering the highest-risk Safe Harbor identifiers at the transcript level, and zero-data retention so no audio remains on vendor servers after processing. Compliance is a shared responsibility: the vendor secures the infrastructure, and you configure the pipeline. Evaluate vendors by asking five questions before routing any patient audio: BAA availability during pilots, default data retention policy, PHI identifier coverage, regional processing boundaries, and training isolation.

GladiaFlow: an open-source voice dictation app for macOS and Windows

You can explain what you want out loud within a few seconds. Typing the same thing takes minutes, and you'll probably rewrite it once. Most of what any of us writes during the workday isn't prose worth crafting, it's Slack replies, AI prompts, commit messages, meeting follow-up. And for all of it, talking is simply faster than typing.

ElevenLabs vs Gladia: speech-to-text comparison for voice AI builders

For teams building voice AI, the pull toward a single vendor for both listening (STT) and speaking (TTS) is real: one API contract, one invoice, one integration surface. ElevenLabs remains a widely used TTS provider, and its Scribe v2 transcription model has closed real ground since launch: new pricing, higher keyterm limits, and independently verified accuracy gains. The question worth asking before consolidating onto one vendor is whether that transcription quality holds up specifically on the noisy, accented, multi-speaker audio your product actually captures. That's exactly where the Gladia and ElevenLabs diverge.

Migrating from Rev.ai to Gladia: what global teams should know

TL;DR: At 10,000 hours of audio per month, Rev.ai's per-hour billing compounds quickly once you add diarization, translation, and sentiment as separate line items. Language coverage gaps surface silently in production when non-English or accented audio degrades without returning an obvious error. This guide gives you the exact API payload mappings, WebSocket transition logic, and a TCO model at realistic scale to make a defensible evaluation of switching. If you decide to migrate, our all-inclusive per-hour pricing bundles every audio intelligence feature at the base rate, and most teams complete the endpoint transition and initial production validation in under 24 hours.